Medical Error and Under-Reporting Causes from the Viewpoints of Nursing Managers: A Qualitative Study
Bibliographic record
Abstract
Background: Patient safety as a goal can be achieved by reporting medical errors (ME); however, most errors are never reported. The aim of this study is to explore the causes of ME, and the obstacles in reporting them amongst nurses. Methods: We conducted semi-structural interviews, with 12 nursing managers in the biggest teaching hospital in southern Iran (2015-2016). The interview guide concentrated on the causes of ME and barriers in reporting them. All face-to-face interviews were recorded and transcribed verbatim and analysed using thematic analysis. Results: In this study 4 main themes were extracted for the causes of ME: personal/social characteristics, nonprofessional practice, hospital related factors/organization contextual factors, and poor management. Also, 5 main themes (such as; personal characteristics, fear from reporting, nonprofessional practices, cultural and social factors, and error surveillance system features) were obtained with regards to barriers in reporting. Conclusions: ME can be reduced by improving professional practice and better human resource management. Also, reporting errors can be increased by focusing on cultural and social factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".